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Small Codes and Large Image Databases for recognition

Small Codes and Large Image Databases for Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Antonio Torralba Rob Fergus Yair Weiss MIT Cambridge MA USA Courant Institute NYU New York NY USA School of Computer Science Hebrew University Jerusalem Israel

the Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. these include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it is possible to perform real-time searches with millions from the Internet using a single large PC and obtain recognition results comparable to the full descriptor. Using our codes on high quality labeled images from the LabelMe database gives surprisingly powerful recognition results using simple nearest neighbor techniques.

关键词： Image databases Image recognition Internet Application software Object recognition computer graphics Layout Standards development Hardware Handheld computers

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Taylor Expansion Based Classifier Adaptation: Application to Person Detection

Taylor Expansion Based Classifier Adaptation: Application to...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Cha Zhang Raffay Hamid Zhengyou Zhang Microsoft Research Redmond WA Georgia Institute of Technology Atlanta GA Microsoft Research Redmond WA USA

Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In this paper, we present a general framework for classifier adaptation, which improves an existing generic classifier in the new test environment. Viewing classifier learning as a cost minimization problem, we perform classifier adaptation by combining the cost function on the old data-sets with the cost function on the data-set collected from the new environment. the former term is further approximated with its second order Taylor expansion to reduce the amount of information that needs to be saved for adaptation. Unlike traditional approaches that are often designed for a specific application or classifier, our scheme is applicable to various types of classifiers and user labels. We demonstrate this property on two popular classifiers (logistic regression and boosting), while using two types of user labels (direct labels and similarity labels). Extensive experiments conducted for the task of person detection in conference-room environments show that significant performance improvement can be achieved with our proposed method.

关键词： Taylor series Testing Detectors Cost function Handwriting recognition Speech recognition Performance evaluation Boosting Face detection Object detection

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Online Learning of Patch Perspective Rectification for Efficient Object Detection

Online Learning of Patch Perspective Rectification for Effic...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Stefan Hinterstoisser Selim Benhimane Nassir Navab Pascal Fua Vincent Lepetit Department of Computer Science Technical University Munich Garching Germany Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

For a large class of applications, there is time to train the system. In this paper, we propose a learning-based approach to patch perspective rectification, and show that it is both faster and more reliable than state-of-the-art ad hoc affine region detection methods. Our method performs in three steps. First, a classifier provides for every keypoint not only its identity, but also a first estimate of its transformation. this estimate allows carrying out, in the second step, an accurate perspective rectification using linear predictors. We show that both the classifier and the linear predictors can be trained online, which makes the approach convenient. the last step is a fast verification -made possible by the accurate perspective rectification- of the patch identity and its sub-pixel precision position estimation. We test our approach on real-time 3D object detection and tracking applications. We show that we can use the estimated perspective rectifications to determine the object pose and as a result, we need much fewer correspondences to obtain a precise pose estimation.

关键词： Object detection Detectors Robustness Image recognition computer science computer vision Laboratories Application software Testing Databases

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Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

Learning Cross-Domain Landmarks for Heterogeneous Domain Ada...

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ieee conference on computer vision and pattern recognition

作者： Yao-Hung Hubert Tsai Yi-Ren Yeh Yu-Chiang Frank Wang Res. Center for IT Innovation Taipei Taiwan Dept. of Math. Nat. Kaohsiung Normal Univ. Kaohsiung Taiwan

ISBN: (纸本)9781467388528

While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. this is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. the effectiveness of our proposed method can be successfully verified.

关键词： Correlation pattern recognition Training Target recognition Adaptation models conferences computer vision

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Image De-fencing

Image De-fencing

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Yanxi Liu Tamara Belkina James H. Hays Roberto Lublinerman Department of Electrical Engineering The Pennsylvania State University USA Department of Computer Science and Engineering Computer Science Department Carnegie Mellon University USA Department of Computer Science and Engineering Pennsylvania State University USA

We introduce a novel image segmentation algorithm that uses translational symmetry as the primary foreground/background separation cue. We investigate the process of identifying and analyzing image regions that present approximate translational symmetry for the purpose of image fourground/background separation. In conjunction with texture-based inpainting, understanding the different see-through layers allows us to perform powerful image manipulations such as recovering a mesh-occluded background (as much as 53% occluded area) to achieve the effect of image and photo de-fencing. Our algorithm consists of three distinct phases-^s(1) automatically finding the skeleton structure of a potential frontal layer (fence) in the form of a deformed lattice, (2) separating foreground/background layers using appearance regularity, and (3) occluded foreground inpainting to reveal a complete, non-occluded image. Each of these three tasks presents its own special computational challenges that are not encountered in previous, general image de-layering or texture inpainting applications.

关键词： Image segmentation Lattices computer science Skeleton Topology Layout Image analysis Airports computer applications Digital images

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Face Shape Recovery from a Single Image Using CCA Mapping between Tensor Spaces

Face Shape Recovery from a Single Image Using CCA Mapping be...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Zhen Lei Qinqun Bai Ran He Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we propose a new approach for face shape recovery from a single image. A single near infrared (NIR) image is used as the input, and a mapping from the NIR tensor space to 3D tensor space, learned by using statistical learning, is used for the shape recovery. In the learning phase, the two tensor models are constructed for NIR and 3D images respectively, and a canonical correlation analysis (CCA) based multi-variate mapping from NIR to 3D faces is learned from a given training set of NIR-3D face pairs. In the reconstruction phase, given an NIR face image, the depth map is computed directly using the learned mapping with the help of tensor models. Experimental results are provided to evaluate the accuracy and speed of the method. the work provides a practical solution for reliable and fast shape recovery and modeling of 3D objects.

关键词： Shape Tensile stress Image reconstruction Humans Surface reconstruction Face detection Surface fitting Magnetic force microscopy Lighting Infrared imaging

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Tensor Reduction Error Analysis - Applications to Video Compression and Classification

Tensor Reduction Error Analysis - Applications to Video Comp...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Chris Ding Heng Huang Dijun Luo Computer Science and Engineering Department University of Technology Arlington TX USA

Tensor based dimensionality reduction has recently been extensively studied for computer vision applications. To our knowledge, however, there exist no rigorous error analysis on these methods. Here we provide the first error analysis of these methods and provide error bound results similar to Eckart-Young theorem which plays critical role in the development and application of singular value decomposition (SVD). Beside performance guarantee, these error bounds are useful for subspace size determination according to the required video/image reconstruction error. Furthermore, video surveillance/retrieval, 3D/4D medical image analysis, and other computer vision applications require particular reduction in spatio-temporal space, but not along data index dimension. this motivates a D-1 tensor reduction. Standard method such as high order SVD (HOSVD) compress data in all index dimensions and thus can not perform the classification and pattern recognition tasks. We provide algorithm and error bound analysis of the D-1 factorization for spatio-temporal data dimensionality. Experiments on video sequences demonstrate our approach outperforms the previous dimensionality deduction methods for spatio-temporal data.

关键词： Tensile stress Error analysis Video compression Application software computer errors computer vision Singular value decomposition Image reconstruction Video surveillance Information retrieval

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A Rank Constrained Continuous Formulation of Multi-frame Multi-target Tracking Problem

A Rank Constrained Continuous Formulation of Multi-frame Mul...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Khurram Shafique Mun Wai Lee Niels Haering ObjectVideo center for Video Understanding Excellence슠at ObjectVideo Reston VA USA

this paper presents a multi-frame data association algorithm for tracking multiple targets in video sequences. Multi-frame data association involves finding the most probable correspondences between target tracks and measurements (collected over multiple time instances) as well as handling the common tracking problems such as, track initiations and terminations, occlusions, and noisy detections. the problem is known to be NP-Hard for more than two frames. A rank constrained continuous formulation of the problem is presented that can be efficiently solved using nonlinear optimization methods. It is shown that the global and local extrema of the continuous problem respectively coincide with the maximum and the maximal solutions of the discrete counterpart. A scanning window based tracking algorithm is developed using the formulation that performs well under noisy conditions with frequent occlusions and multiple track initiations and terminations. the above claims are supported by experiments and quantitative evaluations using both synthetic and real data under different operating conditions.

关键词： Target tracking Inference algorithms Time measurement Layout Optical noise computer vision Video sequences Optimization methods Sensor phenomena and characterization Surveillance

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Classification via Semi-Riemannian Spaces

Classification via Semi-Riemannian Spaces

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Deli Zhao Zhouchen Lin Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Hong Kong China Microsoft Research Asia Beijing China

In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a submanifold embedded in an ambient semi-Riemannian space. the class structures of original samples can be characterized and deformed by local metrics of the semi-Riemannian space. Semi-Riemannian metrics are uniquely determined by the smoothing of discrete functions and the nullity of the semi-Riemannian space. Based on the geometrization of class structures, optimizing class structures in the feature space is equivalent to maximizing the quadratic quantities of metric tensors in the semi-Riemannian space. thus supervised discriminant subspace learning reduces to unsupervised semi-Riemannian manifold learning. Based on the proposed framework, a novel algorithm, dubbed as Semi-Riemannian Discriminant Analysis (SRDA), is presented for subspace-based classification. the performance of SRDA is tested on face recognition (singular case) and handwritten capital letter classification (nonsingular case) against existing algorithms. the experimental results show that SRDA works well on recognition and classification, implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning.

关键词： Linear discriminant analysis Scattering pattern recognition Geometry Covariance matrix Asia Performance analysis Principal component analysis Manifolds Smoothing methods

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Non-Negative Graph Embedding

Non-Negative Graph Embedding

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Jianchao Yang Shuicheng Yang Yun Fu Xuelong Li thomas Huang ECE Department University of Illinois Urbana-Champaign USA ECE Department National University of Singapore Singapore School of CSIS University of London UK

We introduce a general formulation, called non-negative graph embedding, for non-negative data decomposition by integrating the characteristics of both intrinsic and penalty graphs [17]. In the past, such a decomposition was obtained mostly in an unsupervised manner, such as Non-negative Matrix Factorization (NMF) and its variants, and hence unnecessary to be powerful at classification. In this work, the non-negative data decomposition is studied in a unified way applicable for both unsupervised and supervised/semi-supervised configurations. the ultimate data decomposition is separated into two parts, which separatively preserve the similarities measured by the intrinsic and penalty graphs, and together minimize the data reconstruction error. An iterative procedure is derived for such a purpose, and the algorithmic non-negativity is guaranteed by the non-negative property of the inverse of any M-matrix. Extensive experiments compared with NMF and conventional solutions for graph embedding demonstrate the algorithmic properties in sparsity, classification power, and robustness to image occlusions.

关键词： Image reconstruction Iterative algorithms Principal component analysis Tensile stress Matrix decomposition Robustness Scattering Feature extraction Psychology pattern recognition

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